expc_upper_par, expc_upper_comp, expc_par, expc_comp, expc_lower_par, expc_lower_comp = [], [], [], [], [], []
for i in xrange(len(var_par)):
expc_sample_par = [var_par[i][j] for j in xrange(5, 1005)]
expc_par.append(np.mean(expc_sample_par))
expc_lower_par.append(np.percentile(expc_sample_par, 2.5))
expc_upper_par.append(np.percentile(expc_sample_par, 97.5))
expc_sample_comp = [var_comp[i][j] for j in xrange(5, 1005)]
expc_comp.append(np.mean(expc_sample_comp))
expc_lower_comp.append(np.percentile(expc_sample_comp, 2.5) )
expc_upper_comp.append(np.percentile(expc_sample_comp, 97.5))
fig = plt.figure(figsize = (7, 7))
ax_par = plt.subplot(221)
tl.plot_obs_expc_new(var_par['var'], expc_par, expc_upper_par, expc_lower_par, 'partition', True, ax = ax_par)
plt.xlabel(r'Index for $s^2$', fontsize = 10)
plt.ylabel(r'$s_{partition}^2$ / $s_{empirical}^2$', fontsize = 12)
plt.title('Partitions')
ax_comp = plt.subplot(222)
tl.plot_obs_expc_new(var_comp['var'], expc_comp, expc_upper_comp, expc_lower_comp, 'composition', True, ax = ax_comp)
plt.xlabel(r'Index for $s^2$', fontsize = 10)
plt.ylabel(r'$s_{composition}^2$/ $s_{empirical}^2$', fontsize = 12)
plt.title('Compositions')
ax_b_par = plt.subplot(223)
tl_pars_par = tl.get_tl_par_file('out_files/TL_form_partition.txt')
tl.plot_obs_expc_new(tl_pars_par['b_obs'], tl_pars_par['b_expc'], tl_pars_par['b_upper'], \
tl_pars_par['b_lower'], 'partition', False, ax = ax_b_par)
plt.xlabel('Index for b', fontsize = 10)
